The present embodiments relate to feature processing for lung nodules. Features are extracted and used for computer-assisted diagnosis of lung health.
A processor may detect lung nodules from chest x-ray radiographs. A number of automatic lung nodule detection techniques have been developed to help radiologists improve accuracy as well as efficiency in lung cancer screening and diagnosis. Unfortunately, nodule detection in chest x-ray radiographs is a very difficult problem. Automatic nodule detection techniques have difficulty in effectively coping with variations of chest x-ray images. Image characteristics, such as the brightness of a given image, may vary between input images. Nodules may vary, such as appearing larger, smaller, brighter, lighter or weaker, and/or having different shapes. Different background structures, such as ribs, vessels, patient specific lung properties (e.g., scarring, broken rib, or missing lobe), exist.
Feature extraction techniques have been proposed. Extracting discriminating features is important for automatic nodule detection. In general, nodules in chest x-ray radiographs are round-shaped blobs with some limited intensity difference from the rest of the background structures. The nodules may have very little intensity difference from the background. Variations in image characteristics, nodules, and background structures result in significant variations in the combined intensity represented on the x-ray radiograph. As a result, the nodule features are highly position dependent. For example, a peak feature of a nodule in a vessel tree region needs to be more distinguishing for the nodule to be detectable than a peak feature of a nodule in the middle of a lung lobe region. The lung lobe region has ribs and rib crosses, which are more easily distinguished from nodule peaks. The blood of vessels tends to limit x-ray penetration, resulting in weaker indication of a peak.
The positional dependency of the distinction between a lung nodule and the background may be addressed by including position coordinates in feature vectors. Sophisticated learning algorithms may attempt to compensate for such position dependency. Such an approach requires a large data set, and, even if properly trained, may not be sufficiently accurate in practice. It is desirable that such position dependency is fully recovered in classification of genuine nodules and false positives. However, the position dependency is neither well defined nor easy to obtain due to variations in nodules, image properties, and background structures for different images and/or patients.
Intrinsic nodule features that can effectively differentiate genuine nodules from similar background anatomical structures are difficult to extract. Adaptive ring filtering based techniques evaluate the convergence properties of image gradient vectors inside a region of interest around a nodule. This adaptive ring filtering depends on the orientation distribution of the gradient vector and is independent of the intensity and contrast. Some weak and some strong nodules may be captured. However, variation in nodule shape may cause some nodules to be missed.
Matching filter techniques apply a filter or a number of filters with a shape similar to nodules to an input image to enhance the genuine nodules while suppressing false positives and/or other background anatomical structures. Features are then extracted from the enhanced image. Gaussian type of filters, learned (average) nodule shape type filters, and Laplacian of Gaussian (LoG) filters have been used. Filtering may be able to remove a significant number of false positives. However, matched filtering may have limited capability in tolerating complex background structures or in sufficiently enhancing weak nodules.
A snake model may be used to locate a nodule boundary for feature extraction. Unfortunately, nodule boundary localization may be as difficult as nodule detection. Snake models may insufficiently handle background structures and weak nodules.
Other approaches attempt to segment background structures, such as rib cross labeling using segmentation information. The background structure may be removed, at least in part, from the x-ray image. However, other background structures may be difficult to identify and/or remove without also removing nodule information.